Thirty-eight subjects with low-pretest likelihood of cardiomyopathy served as a control group. T1 values were acquired in a single mid-ventricular short axis slice using modified Look-Locker imaging prior and after the application of gadolinium contrast at 1.5 and 3 T. Analysis was performed with regions of interest (ROI) placed conservatively within the septum or to include the whole short axis (SAX) myocardium.

Results: Intra-observer,

inter-observer and inter-study repeated measurements within the septum showed smaller mean differences and narrower 95% confidence intervals than repeated short axis ROI measurements. Native T1 values were higher in septal ROIs compared with SAX values at both field strengths (1.5 T: 976 +/- 37 vs. 952 +/- 41, p < 0.01; 3 T: 1108 +/- 67 vs. 1087 +/- BEZ235 60, p < 0.01). Native T1 values revealed significant mean differences between controls and patients with LVH for both septal (1.5 T: 26 +/- 9, p < 0.01; 3 T: 50 +/- 13, p < 0.01) and SAX ROIs (1.5 T: 19 +/- 11, p < 0.05; 3 T: 47 +/- 19, p < 0.05) with greater differences observed at 3 T versus 1.5 T field strength. Native T1 values

revealed significant mean differences between controls and patients with DCM for septal ROI (1.5 T: 29 +/- 15, p < 0.05; 3 T: 55 +/- 16, p < 0.01) at both 1.5 T and 3 T, but only for SAX ROIs at 3 T (49 +/- 17, p < 0.01). There were no significant differences in post-contrast T1 values or Nepicastat partition coefficient (.) between controls and patients.

Conclusion: Conservative septal ROI T1 measurement is a robust technique with excellent intra-observer, inter-observer and inter-study reproducibility for native and post-contrast T1 value and partition coefficient measurements. Moreover, native septal T1 values reveal the greatest difference between normal and abnormal myocardium, which is independent of geometrical alterations of cardiac chamber and wall thickness. We propose the use of native T1 measurements using conservative septal technique as the standardized

approach to distinguish health from disease assuming diffuse myocardial involvement.”
“Cancer diagnosis is based on visual examination under a microscope of tissue sections from biopsies. But whereas pathologists rely on tissue stains eFT-508 to identify morphological features, automated tissue recognition using color is fraught with problems that stem from image intensity variations due to variations in tissue preparation, variations in spectral signatures of the stained tissue, spectral overlap and spatial aliasing in acquisition, and noise at image acquisition. We present a blind method for color decomposition of histological images. The method decouples intensity from color information and bases the decomposition only on the tissue absorption characteristics of each stain. By modeling the charge-coupled device sensor noise, we improve the method accuracy.